July 2025 performance summary for DUNE/larnd-sim: Delivered significant feature enhancements to neighboring pixel calculation and diffusion modeling, refined time handling and numerical precision, expanded track data export, and enhanced diffusion granularity. Implemented clearer warning messaging. These changes improve charge-response coverage, numerical stability, and downstream analysis readiness, directly contributing to more accurate simulations, faster diagnostics, and robust data pipelines.

May 2025 performance summary focusing on key features and business value for DUNE/larnd-sim.

April 2025 monthly summary for DUNE/larnd-sim focused on improving simulation fidelity, standardizing detector configuration, and cleaning the codebase to boost reliability and maintainability. Delivered key features, addressed timing-related issues, and streamlined configuration/assets to support faster feature delivery and easier onboarding. Key outcomes by area: - Drift time readout timing accuracy: Refactored drift_time calculation to make t_start/t_end explicit and to depend solely on drift_time and t0, improving readout timing accuracy in the simulation. - 2x2 detector configuration: Standardized and modernized configuration, removed outdated parameters, updated loading for time-dependent signals, added single-threshold support, and aligned YAML/config with detector response data versions. - Detector response data assets: Updated assets with new binary detector response data files and cleaned obsolete data, ensuring assets match new formats and configuration. - Code cleanup: Removed unused functions and large commented CUDA/Numba code blocks across simulate_pixels.py, detector.py, sim.py, and dtsim.py to simplify maintenance and reduce latent bugs. Business value and impact: - Increased simulation fidelity and reproducibility, enabling more accurate performance assessments and faster iteration. - Reduced maintenance burden and latent bug surface through targeted refactors and cleanup. - Improved onboarding potential for new contributors through clearer configuration and up-to-date data assets. Technologies/skills demonstrated: - Python/C++-level simulation maintenance, refactoring, and cleanups - YAML/config management and data asset alignment - Handling detector response data formats and versioning - Collaboration with data/asset updates and code cleanup

February 2025 performance summary: Delivered calibration, configuration, and data handling enhancements for DUNE ND LAr flow and larnd-sim. Achievements include introducing a CalibNoiseFilter YAML and integrating it into the final calibration MC workflow, aligning detector pixel layouts across FSD and NDLAr to eliminate misconfiguration, refactoring data management for charge reconstruction with conditional dataset references, standardizing pixel layout configuration for ND LAr across repos, fixing batch processing event flag handling to ensure reliable trigger writes, and improving data processing in DumpTree.py with trajectory mapping fixes. These changes improve calibration accuracy, detector configuration reliability, data integrity, and processing performance, enabling more reproducible simulations and faster workflows. Technologies used: YAML config, Python data handling, layout mapping, batch processing logic, dtype alignment, trajectory tracking, and code refactoring.

January 2025 focused on strengthening data integrity in Monte Carlo simulation workflows within DUNE/ndlar_flow. Delivered a critical bug fix that validates Monte Carlo (MC) channel keys, raising an error when any MC channel key is NaN to prevent invalid data from entering the processing pipeline. This change improves robustness of simulation data processing and reduces downstream failures by ensuring only valid channel keys are used.

Month: 2024-12 Concise monthly summary focused on delivering business value and technical accomplishments for DUNE/ndlar_flow. In December, the team implemented a critical bug fix for the CalibNoiseFilter that improves data integrity and downstream analysis readiness. The changes enhance the accuracy of Monte Carlo truth association for filtered hits and streamline setup/filter logic with minor cosmetic refinements.

Month: 2024-10 — Focused on improving the reliability and clarity of the Event Display in the DUNE/ndlar_flow repo. Delivered robust geometry handling using the flow default configuration, with a safe fallback for missing geometry, and refined the final hits color scale to improve data representation. These changes reduce UI fragility and enhance data visualization for faster interpretation by downstream teams.